| 其他摘要 | A series of eco-environmental problems, such as severe soil erosion, decreases in soil
fertility and productivity, land degradation and desertification, have threatened the
sustainable development of the ecosystems and social economy on the Loess Plateau of
China. Regional and local projects aiming on ecological restoration and reconstruction
have been launched to combat these problems on this area, practically through land use
optimization, vegetation recovery and soil and water conservation. Soils on the Loess
Plateau are poverty due to the scarcity of both soil nutrients and water resources. These
two factors together greatly limit plant growth and agricultural production, controlling the
spatial variations of land use and vegetation. From regional perspective, systematic and
accurate information on the spatial variations of soil nutrients and the impact factors at
different scales is basic and essential for effective applications of these ecological projects,
and would be helpful in related macro policy makings.
The main purposes of this dissertation were to (1) explore the current level and stocks
of several main soil nutrients across the entire Loess Plateau region; (2) to reveal the
spatial variability of these soil nutrients and illustrate their distribution patterns; (3) to
investigate the relationships between these soil nutrients and pertinent environmental
factors at different scales; (4) and to generate accurate prediction models using
easy-to-measure variables. An intensive soil survey with a sampling interval of about
30-50 km was accomplished within one year during 2008, by investigating 382
representative sampling sites across the entire Loess Plateau region (62.4 × 10 4 km 2 ). A
total of 1528 composite soil samples were corrected using a handy soil auger (5 cm in
diameter) from 0-20 cm and 20-40 cm topsoil layers, and 0-100 cm and 100-200 cm deep
soil layers. Additionally, 764 undisturbed soil cores were collected with cutting rings (100 cm 3 in volume). The environmental conditions of each sampling site were recorded, such
as latitude, longitude, elevation, aspect, slope gradient, slope position, land use type and
vegetation type. All the soil samples were taken to the laboratory for measurements of soil
organic carbon (SOC), soil total nitrogen (STN), soil total phosphorus (STP), soil total
potassium (STK), soil pH, soil mechanical composition and bulk density (BD). Traditional
statistics methods (correlation analysis, linear regression, ANOVA and post-hoc),
geostatistics methods (semivariogram, kriging interpolation, factorial kriging analysis) and
state-space modeling approach were used for spatial analysis and generation of prediction
models. The main resultes are listed as follows:
(1) Soil organic carbon concentrations (SOCC) varied within a wide range throughout the
region from 0.38 g kg -1 to 54.03 g kg -1 , with mean values of 10.34 g kg -1 and 6.78 g
kg -1 for the topsoil (0-20 cm) and subsoil (20-40 cm), respectively. The mean SOC
density (SOCD) was 2.64 kg m -2 in the 0-20 cm soil layer and 4.57 kg m -2 in the 0-40
cm soil layer, and it was estimated that 1.64 and 2.86 Pg (1Pg = 10 15 g) of organic
carbon were stored in these soil layers, respectively. Estimates for deeper soil layers
indicated that mean SOCD in the 0-100 cm and 0-200 cm layers was 7.70 and 12.45 kg
m -2 , respectively, while the total organic carbon stocks amount to 4.78 Pg (0-100 cm)
and 5.85 Pg (0-200 cm), respectively. The SOC stocks in the 0-20 cm and 0-100 cm of
soils in the Loess Plateau region contribute 0.36% and 0.31% to the global SOC stored
in these respective layers. In addition, our results indicated that the 0-20 and 0-100 cm
soil layers of the Loess Plateau, which covers nearly 6.5% of the area of China,
currently holds about 8.21% and 5.32% of the total SOC stocks in these layers in China,
respectively. Coefficient of variation values showed moderate variation for both SOC
concentration and density values in both 0-20 cm and 20-40 cm soil layers. Significant
correlations were detected between SOCC and these environmental variables, notably
with soil total nitrogen (STN), soil pH and clay content. Multiple linear regression
analysis indicated that STN, clay content, soil pH, elevation and temperature had
greater effects on regional SOCC variability, among all the selected soil and site
variables. The results of ANOVA showed that precipitation, temperature, elevation,
clay plus silt contents and land use showed significant regional impacts on SOCD. The
results also show that human activities have heavily affected SOC accumulation. Measured SOCD under cropland was relatively higher than under grassland and
forestland.
Geostatistics analysis showed that the maximum autocorrelation ranges were 384
km, 393 km and 339 km for SOCC (0-20 cm and 20-40 cm) and SOCD (0-40 km),
respectively. Nugget-to-sill ratios were 0.52, 0.50 and 0.45, which also indicated
moderate spatial dependence. The distribution maps of SOCC in both topsoil layers and
SOCD in 0-40 cm soil layers were produced by geostatistical method showed that the
overall spatial pattern was characterized by an area of low SOC content surrounded by
bands with higher values that generally increased towards the region’s boundaries. The
distribution pattern corresponded to that of the major regional landforms, which also
influenced land use, whereby the sandy Ordos Plateau is surrounded by relatively
fertile plains and valleys, where the human population density is highest, and the
regional boundary is mountainous.
(2) In 0-20 cm soil layers, mean STN concentrations (STNC) and STP concentrations
(STPC) ranged from 0.58 g kg -1 to 0.81 g kg -1 and from 0.50 g kg -1 to 0.73 g kg -1 ,
respectively, under different land types. In 20-40 cm soil layers, mean STNC and STPC
ranged from 0.46 g kg -1 to 0.60 g kg -1 and from 0.48 g kg -1 to 0.61 g kg -1 , respectively.
Mean STN and STP densities in 0-40 cm soil layers ranged from 0.27 kg m -2 to 0.39 kg
m -2 and from 0.27 kg m -2 to 0.38 kg m -2 , respectively, under different land use types.
All the concentrations and densities of STN and STP under different land use types
showed moderate variations, which was indicated by the values of coefficient of
variation. We detected significant (p<0.05) effects of land use, precipitation and
temperature on both STN and STP. But the results varied among different precipitation
and temperature regions and different land use types. Generally, croplands had higher
concentrations and densities of STN and STP than forestlands and grasslands, and
regions with more precipitation and higher temperature had higher STN and STP
densities. Significant correlations were found between STN, or STP, with selected
factors, i.e. soil organic carbon, precipitation, temperature, elevation, latitude, longitude,
slope gradient, clay content, silt content and soil pH. The results were not consistent
within either the variable or the land use types. We generated land-use specific linear
models to predict STN and STP using these related variables. Geostatistical analysis showed moderate spatial dependence of both STN and STP, indicated by the values of
nugget to sill ratio. The spatial range of STN and STP ranged from 374 km to 461 km
and 546 km to 664 km, respectively. This range was much larger than our sampling
intervals (30-50 km). The distribution maps of STN and STP densities were made with
kriging interpolation. Finally, the stock of STN and STP was estimated to be 0.217 Pg
and 0.205 Pg in the upper 0-40 cm soil layers, which was about 5.4% and 7.3% of the
total nitrogen and phosphorus stocks in China. Our study suggests that it is important to
take land use into account when considering variation of STN and STP at regional
scale.
(3) In 0-20 cm and 20-40 cm soil layers, soil total potassium (STK) concentration varied
from 10.07-30.97 g kg -1 and 12.82-32.39 g kg -1 , with mean values of 19.25 g kg -1 and
19.10 g kg -1 , respectively. The coefficients of variation for STK were 13.4% and 13.3%,
defined as moderate variation. The spatial ranges of STK were 546 km and 564 km.
The nugget-to-sill ratios were 31.7% and 26.9%, showing moderate spatial dependence.
Two methods, state-space modeling and classical linear regression, were used to
quantify the relationships between STK (0-20 cm) and bulk density, clay and silt
content, soil pH, precipitation, temperature, and elevation. The best state-space models
explained more than 97% of the STK variation, while the best linear regression model
explained less than 26% of the STK variation. The results showed that all the
state-space models described the spatial variation of STK much better than the
corresponding linear regression models. Temperature, bulk density and clay content
were identified as important factors that affected localized variation of STK, since they
were connected to the better performance of the state-space models. State-space
modeling is recommended as a useful tool for quantifying spatial relationships between
soil properties and other environmental factors in large-scale regions.
(4) In 0-20 cm soil layers, soil pH values ranged from 6.06 to 10.76, with a mean of 8.49
and a median of 8.48. Land use type had a significant effect (p < 0.01) on soil pH;
grassland soils had higher pHs than cropland and forestland soils. From a regional
perspective, soil pH showed weak variation and strong spatial dependence, indicated
by the low values of the coefficient of variation (5%) and the nugget-to-sill ratios
(<0.25). Indices of cross-validation, i.e. average error (AE), mean absolute error (MAE), root mean square error (RMSE) and model efficiency coefficient (MEC) were
used to compare the performance of the four different interpolation methods, i.e.
inverse distance weighting (IDW), splines, ordinary kriging and cokriging. The results
showed that kriging methods interpolated more accurately than IDW and splines.
Cokriging performed better than ordinary kriging and the accuracy was improved by
using soil organic carbon as an auxiliary variable. Regional distribution maps of soil
pH were produced. The southeastern part of the region had relatively low soil pH
values, probably due to higher precipitation, leaching, and higher soil organic matter
contents. Areas of high soil pH were located in the north of the central part of the
region, possibly associated with the salinization of sandy soils under inappropriate
irrigation practices in an arid climate.
(5) Traditional statistical analysis of the correlations between spatially distributed variables
takes no account of their regionalized nature. Factorial kriging analysis (FKA) was
applied to investigate scale-dependent correlations between selected soil properties (i.e.
soil organic carbon (SOC), soil total nitrogen (STN), soil total phosphorus (STP), soil
total potassium (STK), soil pH, bulk density (BD), and clay and silt contents) and
environmental factors (i.e. elevation, precipitation, temperature, land use type and soil
type). A linear model of coregionalization, including a nugget effect and two spherical
structures (effective ranges of 200 and 400 km), was fitted to the experimental auto and
cross-variograms of the variables. Scale-dependent correlations were calculated for
nugget effect scale (<30-50 km), short-range scale with a range of 200 km and
long-range scale with a range of 400 km. Principal component analysis was conducted
to clearly illustrate the correlations at each spatial scale. The scale-dependent
correlations were different from the general correlations and varied at different scales.
Generally, SOC and STN were strongly correlated at the nugget effect scale and the
long-range scale, but not at the short-range scale. Precipitation and clay content showed
close correlations with STP at the nugget effect scale and long-range scale. The STK
was weakly correlated with the other variables at each spatial scale, but closely
correlated with soil type at the long-range scale. Soil pH was closely correlated with
BD, soil type and elevation at the nugget effect, short and long spatial scales,
respectively. Close correlations were found between BD and land use type at each spatial scale. Land use and soil type were considered to be the important factors
controlling spatial variation of soil properties at the short-range scale while at the
long-range scale the likely factors were identified as precipitation, temperature and
elevation.
Based on intensive filed sampling and uniform laboratory measurements, our study
provided an overview on the spatial variation and impact factors of soil organic carbon,
soil total nitrogen, soil total phosphorus, soil total potassium and soil pH across the entire
Loess Plateau region of China. The reliable spatial data updated the soil database for the
study region, and can be used as important input layers in regional digital soil mapping,
carbon and nitrogen cycle modeling and evaluation of the potential non-point source
pollution associated with soil erosion. Moreover, the results presented in this dissertation
can serve as an important background for the future studies in related fields, and can be
useful in macro decision making for regional eco-environment restoration on the Loess
Plateau.
Keywords: The Loess Plateau, Soil nutrients; Spatial variability; Scale dependency, Land
use; Geostatistics; State-space modeling |
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